CAREER: Establishing Coherent Frameworks for Massive Galaxy Formation and Inclusive Astronomy

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Studies on how galaxies form show that early galaxies were surrounded by large gas reservoirs that were sustained by the cosmic web, which should have allowed for a steady formation of new stars. However, only three billion years after the Big Bang, half of these massive galaxies stopped forming new stars. The goal of this program is to better understand these galaxy ecosystems on a larger scale and figure out why they formed so quickly and stopped forming stars so early. This team will use data from the Large Millimeter Telescope and Subaru Telescope to map the distribution of galaxies and their molecular gas throughout most of the history of the universe. They will also establish a program at the UMass Amherst to provide research opportunities for underrepresented groups in STEM fields. They will conduct annual workshops and externships for girls from low-income school districts, giving them hands-on experience with real data. This will help to promote inclusion and equity for all aspiring astronomers. The investigator will address fundamental questions about massive galaxy evolution. By mapping degree-scale statistical populations of massive galaxies, the investigator will connect their environments from the Subaru Telescope/Prime Focus Spectrograph to their cold gas, as traced by dust using the TolTEC instrument on the Large Millimeter Telescope. By using the most sophisticated cosmological simulations to date that include the physics of dust formation, growth, and destruction, the investigator will further perform an apples-to-apples analysis of mock galaxies to understand systematics and calibrate observations. Through linking star formation and cold gas across diverse galaxy habitats, the investigator will develop a physical model explaining the key processes responsible for the assembly of early massive galaxies through to the present day. These three pillars combined allow us to mimic observational techniques on simulated data — this is the only way to both map wide enough area but also to control for systematic uncertainties. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.